Free space optical communications involve using light to transmit data across the open distance between two points, in a line-of-sight manner, and without the use of an optical waveguide or other medium. Typically, transmissions are made through the atmosphere or in space/vacuum using a laser system, or, in some short-distance cases, LED's or the like. For a laser communication system, encoded binary data is transmitted using modulated laser beams that travel from the sending station to the receiving station. In the case where one or both of the stations are in motion, e.g., an aircraft and a ground terminal, satisfactory transmission relies on the ability to precisely point the optical equipment at the remote station, such that the energy of the laser is received with minimal loss at the remote station.
The initial process of target location and optical pointing is known as “acquisition.” The ongoing process of following the movements of the remote station over time is known as “tracking.” The problem is to design an algorithm for acquisition and tracking that increases the time during which data can be reliably transmitted between remote stations, taking into account the intervals when laser transmissions are obstructed or are disabled for varying time durations. In the past, tracking algorithms used a tracking filter technique (e.g., a Kalman filter) to extrapolate from the last known position and velocity, to predict future positions of the remote (moving) station. When the tracking filter could no longer be used due to the excessive error growth of estimated values, an attempt was made to select a single “approaching” waypoint in the expected path of the remote station. In particular, re-acquisition after loss of tracking lock typically involves pointing the optical terminal to “stare” at a location that the remote station is expected to traverse. The Kalman filter can provide such a location only for a limited time after tracking lock has failed and no new position data has been received from the remote station during that interval, e.g., over a supervisory channel. After this time the Kalman filter's location estimate is not reliable because the variances of these estimates have grown too large and/or the motion model (e.g., constant acceleration) is not valid. Beyond that point in time another strategy is required for re-acquisition.